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There’s always something exciting and energizing in the air when we flip the calendar to September, and this year was no exception. Sure, bidding farewell to long sunny days and a slightly slower pace can make anyone a bit wistful, but not for long—not when there’s so much happening in the ML and AI scene, so many new tools and innovations to learn about, and lots of new skills to develop.
We’re thrilled to share our most-read and -shared articles of the past month in case you missed any of them (or just want to revisit a favorite or two). Even more than usual, they represent the full breadth of topics our authors cover, from core programming skills to cutting-edge LLM techniques, so we’re certain that you’ll find something in our September highlights to pique your interest. Happy reading, and here’s to a new season full of learning and growth!
Monthly Highlights
- How to Implement Graph RAG Using Knowledge Graphs and Vector Databases
Our top read of the month came from Steve Hedden: a clear and accessible step-by-step tutorial on implementing retrieval-augmented generation (RAG), semantic search, and recommendations. - Data Scientists Can’t Excel in Python Without Mastering These Functions
There’s always room for another solid Python tutorial — and Jiayan Yin’s compendium of key functions for data scientists proved especially helpful for our readers. - Python QuickStart for People Learning AI
More Python! Shaw Talebi’s beginner-friendly guide focuses on the programming topics you’ll need to master if your end goal is to develop custom AI projects and products. - Automated Prompt Engineering: The Definitive Hands-On Guide
Interested in learning how to automate prompt engineering and unlock significant performance improvements in your LLM workload? Don’t miss Heiko Hotz’s practical guide.
- GenAI with Python: Build Agents from Scratch (Complete Tutorial)
Leveraging the combined power of Ollama, LangChain, and LangGraph, Mauro Di Pietro walked us through the entire workflow of creating custom AI agents. - SQL: Mastering Data Engineering Essentials (Part I)
Whether you’re new to SQL or could use a good refresher, Leonardo Anello’s comprehensive introduction, aimed specifically at data engineers, is a powerful, one-stop resource. - Choosing Between LLM Agent Frameworks
What are the tradeoffs between building bespoke code-based agents and relying on the major agent frameworks? Aparna Dhinakaran shares practical insights and recommendations on a key question. - Analytics Frameworks Every Data Scientist Should Know
Drawing on her previous experience as a consultant, Tessa Xie offers data professionals helpful pointers on “how to break down an abstract business problem into smaller, clearly defined analyses.” - Beyond Line and Bar Charts: 7 Less Common But Powerful Visualization Types
From bump charts to circular bar plots and Sankey diagrams, Yu Dong invites us to expand our visual-design vocabulary and experiment with less-common visualization approaches. - 5 Tips To Make Your Resume *Really* Stand Out in FAANG Applications
In a competitive market, every detail counts, and small adjustments can make a major difference—which is why you should explore Khouloud El Alami’s actionable advice for current job seekers.
Our latest cohort of new authors
Every month, we’re thrilled to see a fresh group of authors join TDS, each sharing their own unique voice, knowledge, and experience with our community. If you’re looking for new writers to explore and follow, just browse the work of our latest additions, including Alexander Polyakov, Harsh Trivedi, Jinhwan Kim, Lenix Carter, Gilad Rubin, Laurin Brechter, Shirley Bao, Ph.D., Iqbal Rahmadhan, Jesse Xia, Sezin Sezgin-Rummelsberger, Reinhard Sellmair, Yasin Yousif, Hui Wen Goh, Amir Taubenfeld, Sébastien Saurin, James Gearheart, Zackary Nay, Jens Linden, PhD, Eyal Kazin, Dan Beltramo, Sabrine Bendimerad, Niklas von Moers, Milan Tamang, Abhinav Prasad Yasaswi, Abhinav Kimothi, Miguel Otero Pedrido, Oliver Ma, Hamza Farooq, Shanmukha Ranganath, Maarten Sukel, Murilo Gustineli, Luiz Venosa, Saankhya Mondal, David Vaughn, Prasad Mahamulkar, Federico Rucci, Philippe Ostiguy, M. Sc., Anurag Bhagat, and Megan Grant, among others.
Thank you for supporting the work of our authors! We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.
Until the next Variable,
TDS Team
Graph RAG, Automated Prompt Engineering, Agent Frameworks, and Other September Must-Reads was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
